The adaptive parameter kernel processor relates generally to enhancing the signal-to-noise ratio of communication signals. More particularly, the present invention relates to statistical evaluation techniques incorporating probability density functions to suppress interference in communications signals.
Radios may receive combinations of three forms of signals: noise, interference and communications. Noise, created in the atmosphere due to natural causes such as lightning and the like, is unpredictable. Interference signals can be caused inadvertently, such as by several stations broadcasting within the same band, or can be caused deliberately, such as by an adversary transmitting signals to mask communications signals.
Many major sources of signal interference are non-Gaussian in structure. One technique for detecting communications signals in the presence of non-Gaussian interference employs algorithms that estimate the statistics of the interference. This estimate is then used to improve the signal-to-noise ratio of the received signals.
One estimation technique utilizes a probability density function (PDF) to generate a gain factor from samples of a signal magnitude, such as amplitude or phase. Adaptive Locally Optimum Detection (ALOD) algorithms incorporate PDFs to suppress interference, but have the disadvantage that no fixed set of PDF parameter values is optimum for all signal environments. There is thus a need for an interference suppression system that can optimize the PDF parameter values in any signal environment.